Single image dehazing using multi-enhanced image stacking and pyramidal fusion

Abstract

Imaging systems often generate images with low contrast, particularly in adverse weather conditions such as haze and fog. The reduced perceptibility in these images is affected by the dispersion and absorption of the light due to particles in the atmospheric aerosols. Due to the absence of depth information, image dehazing poses a challenge. To address this, fusion-based methods are employed as they can integrate complementary features from multiple enhancement techniques, allowing for a more comprehensive and balanced enhancement of both local details and global contrast. However, challenges such as uneven illumination, artifacts, and the preservation of fine details persist in many dehazing scenarios, motivating the need for more robust solutions. A novel fusion-based approach that uses multiple image enhancement techniques to improve the perceptual quality of the dehazed image is proposed here. Initially the Sharpening Smoothing Image Filter (SSIF) is applied on the input image for performing the sharpening operation. Then an intermediate image stack consisting of six enhanced images is developed using the sharpened image by applying Gamma Correction with four different gamma values, Contrast Limited Adaptive Histogram Equalization (CLAHE) and Multi-Scale Retinex with Color Restoration (MSRCR). This intermediate stack generation ensures that each enhancement technique contributes uniquely to the final result. A pyramidal fusion is applied to the images in the intermediate stack using contrast, saturation, and well-exposedness to produce the dehazed image. The fusion process effectively minimizes local haze variations, preserves color fidelity, and enhances image contrast under varying lighting conditions. The experimental analysis using five different datasets shows that the proposed method outperforms both qualitatively and quantitatively compared to existing methods.